Transient Stability Probability Of Power System

In recent years, blackouts happened in the world frequently, which force people to find a much more effective and scientific approach to analyze the security and stability of electric power systems. Because of the uncertainty of much information in power systems, it is more meaningful to adopt the method of probability to assess the security and stability of power systemsThere are usually two kinds of probability methods to analyze dynamic security of power system– the method of simulation and the method of analytics. The method of Monte-Carlo is a popularly used method of simulation. Although this approach can flexibly involves many stochastic factors, this approach needs much simulation. Much achievement about the method of analytics has been acquired and three steps are usually taken when this method is used. First, the occurrence probability of every forecasting fault in the forecasting faults set is supposed to be calculated. Second, the probability of system transient instability after one fault occurs is supposed to be calculated. Third, the composite probability index of power system is calculated. Currently, although there is certain achievement on method of analytics, the probability model of power system transient instability after occurrence of one fault still needs to be studied and perfected.Firstly, according to the fact that to calculate the probability of system transient instability is a basic step in the probabilistic assessment of power system security, this paper proposes a transient instability probability model combining discrete and continuous stochastic variables based on the study outcomes of DSR (Dynamic Security Region). Besides the uncertainty of load forecasting at each node, the uncertainty of the location of fault occurrence and the uncertainty of fault-clearing time, the uncertainty of load model is considered. Furthermore, the method of Gram-Charlier progression based on the cumulants is adopted to calculate the joint probabilistic distribution of truncated normal distribution, which is more up to the reality. Finally, the efficiency of this approach has been proved.Secondly, based on the method proposed in this paper to calculate the probability of system transient instability after a forecasting fault occurs, the order of influence of each forecasting fault on power system security is shown in this paper. The index of the influence of each forecasting fault on power system security involves not only the occurrence probability of each fault but also the instability probability of the power system after occurrence of one fault.